Considering the problem that traditional active learning algorithms can only handle small and medium size data sets, a big data active learning algorithm based on MapReduce was proposed. Firstly, a classifier was trained by Extreme Learning Machine (ELM) algorithm on an initial training set, and the outputs of the classifier were transformed into a posterior probability distribution by softmax function. Secondly, the big data set without labels was partitioned into l subsets, which were deployed to a cloud computing platform with l nodes. On each node, the information entropies of instances of each subset were calculated by the trained classifier, and q instances with maximum information entropies were selected for labeling, then the l× q labeled instances were added into the training set. Repeat the above steps until the predefined termination criterion was satisfied. Contrast test with ELM-based active learning algorithm were conducted on 4 data sets including Artificial, Skin, Statlog and Poker. Experimental results show that the proposed algorithm can complete active instance selection on 4 data sets, while the active learning algorithm based on ELM can only complete active instance selection on the smallest data set, indicating that the proposed algorithm outperforms the active learning algorithm based on ELM.

Due to the dynamic characteristic of Virtual Network Request (VNR) resources, a Virtual Network Embedding algorithm based on Dynamic Virtual Network Requests (DVNR-VNE) was proposed. On the basis of mixed linear programming theory, we adopted multi-queue to pre-process different types of VNRs and established a multi-object embedding model with minimum mapping and migration cost. Those requests which need to release resource would be accepted firstly to support more VNRs, and the new arrived VNR would be embedded by an optimized WinDow-Virtual Network Embedding (WD-VNE) algorithm. The simulation results show that the proposed algorithm can reduce link cost, migration cost and can also obtain higher accept ratio.

In order to ensure the security of infrared image in infrared imaging systems and avoid the shortages of traditional image encryption method such as low security and poor real-time performance, a new encryption scheme for infrared image by using Lorenz chaotic system was proposed based on the analysis of bit plane features of infrared image. In this scheme, based on the influence factor statistical characters of bit plane of infrared image, the abscissa, ordinate and bit plane of image's higher four bit planes were scrambled through one operation by using Lorenz chaotic system. This scheme extended image encryption form the pixel level to the bit level. Compared to traditional image encryption method, the scheme is based on the special bit plane statistical character of infrared image, and has fast encryption speed and good performance. It can resist exhaustive attack effectively and has good reliability and scrambling degree. The scheme can be applied to the infrared monitoring system with high security requirements to improve system security and prevent hacker intrusion effectively.

To deal with the energy-efficient routing selection problem of the Wireless Sensor Network (WSN), an Energy-Efficient routing algorithm with Location information and Double cluster heads based on Hybrid Energy-Efficient Distributed clustering (HEED-EELD) was proposed. Assuming that all the network nodes had location awareness capabilities, the network was divided into different hierarchies according to the best single-hop distance, so the nodes determined their hierarchies based on their locations. Double cluster heads were selected to share a single cluster head's work and to balance the energy consumption. In the inter-cluster multi-hop routing, the cluster head selected the optimal route based on location, distance and cost function about residual energy. Matlab simulation results show that, compared with Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm, HEED algorithm, HEED-EELD has obvious advantages in terms of network lifetime, energy efficiency and energy balancing.

To solve the problem of receiver buffer blocking and load unbalance of Concurrent Multipath data Transfer using Stream Control Transmission Protocol (CMT-SCTP) in heterogeneous network environments, an improved round-robin data scheduling algorithm was proposed. The network condition of each path was estimated by the proposed algorithm according to the sender queue information and the congestion status of links. Then the proposed data scheduling algorithm distributed the transmission task to each path based on its network condition, curtailed the queuing time of data chunks in sender buffer and reduced the number of out-of-order data chunks in receiver buffer. Simulation results show that the improved round-robin data scheduling algorithm can effectively enhance the transmission efficiency of CMT-SCTP in a heterogeneous wireless network environment and mitigate the receiver buffer blocking problem. It can also adapt to different network conditions.

A static priority scheduling algorithm for periodic priority exchange was proposed to resolve the low-priority task latency problem in real-time multi-task system. In this method, a fixed period of timeslice was defined, and the two independent tasks of different priorities in the multi-task system exchanged their priority levels periodically. Under the precondition that the execution time of the task with higher priority could be guaranteed, the task with lower priority would have more opportunities to perform as soon as possible to shorten its execution delay time. The proposed method can effectively solve the bad real-time performance of low-priority task and improve the whole control capability of real-time multi-task system.

Resource allocation strategy has been the hot and difficult research topic in the field of cloud computation. Research and analysis of current resource allocation strategies were carried out. Firstly, the challenges facing cloud platform resource allocation were analyzed. Then the formalization description of cloud platform resource allocation was given. The current mainstream of resource allocation strategies were described from the perspectives of heuristic allocation algorithms, economic theory based strategy, and other allocation strategies, and their advantages and disadvantages were discussed. Finally, a comprehensive comparison of the mainstream strategies based on specific indicators was made, and some future research directions were pointed out.

Most of the current approaches to evaluate the degree of image scrambling depend on original images. And there are no scientific mathematical models as their theoretic basis. A linear model for difference statistic distribution of ideal scrambled image was put forward in this paper by analyzing the difference statistic distribution of scrambled image. Furthermore，three methods were presented based on this model to evaluate image scrambling degree. The first one was the absolute difference of slope, the second was the absolute difference of difference, and the third was method of overlapping area. The experimental results indicate that these methods are very sensitive to the statistical distribution of image difference, and they are independent of original image with good agreement with human vision system, so they can achieve blind evaluation for image scrambling degree objectively.